Domino Data Lab

JANUARY 24, 2019

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

CIO Business Intelligence

JUNE 27, 2022

It’s not perfect by any means, and we are continuously breaking our own product, but it’s optimized for shipping new features to customers as quickly as we can. You pointed to frontend as a key area in 2019. Is it still true today? . Cramer: Yes, it’s still true today. We rely heavily on automated testing.

Software 115

Software Experimentation Testing Marketing 115

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Remember that digital transformation is about transforming your business and operating models with technology.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . ModelOp — Governs, monitors, and orchestrates models across the enterprise. Meta-Orchestration .

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Introduction. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Ensure a culture that supports a steady process of learning and experimentation. Here’s our CFP , open through February 26. ethics in AI.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. For instance, if I’m reading a paper from 2019, a popular song from that year could start playing. Setting the Stage: Who Am I and What Am I Trying to Build? This choice also inspired me to call my project Swift Papers.

Consulting 246

Consulting Interactive Software IT 246

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. Ontotext develops re-usable domain models as pre-packaged knowledge graphs.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. The first 18 years: Develop vision and products and deliver to innovation leaders.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Corinium

APRIL 25, 2019

All assets need to be optimally leveraged for maximum business value while also being protected from misuse, whether there was malicious intent or not, and this needs to be the responsibility of whomever is responsible for that asset in the company. This is a much more proactive and scalable model.

Insurance 150

Insurance Risk IoT Cost-Benefit 150